char_conv_layers – the number of convolutional layers on character level

char_window_size – the width of convolutional filter (filters).
It can be a list if several parallel filters are applied, for example, [2, 3, 4, 5].

char_filters – the number of convolutional filters for each window width.
It can be a number, a list (when there are several windows of different width
on a single convolution layer), a list of lists, if there
are more than 1 convolution layers, or None.
If None, a layer with width width contains
min(char_filter_multiple * width, 200) filters.

char_filter_multiple – the ratio between filters number and window width

char_highway_layers – the number of highway layers on character level

conv_dropout – the ratio of dropout between convolutional layers

highway_dropout – the ratio of dropout between highway layers,

intermediate_dropout – the ratio of dropout between convolutional
and highway layers on character level

lstm_dropout – dropout ratio in word-level LSTM

word_vectorizers – list of parameters for additional word-level vectorizers,
for each vectorizer it stores a pair of vectorizer dimension and
the dimension of the corresponding word embedding

word_lstm_layers – the number of word-level LSTM layers

word_lstm_units – hidden dimensions of word-level LSTMs

word_dropout – the ratio of dropout before word level (it is applied to word embeddings)